Abstract Since isometric mapping (ISOMAP) has no supervision and explicit mapping function and other limitations, an improved algorithm, selection of vector quantization landmark points for supervised isometric mapping with explicit mapping (SE-VQ-ISOMAP), is put forward. Firstly, the category information is introduced in the construction of neighborhood graph and geodesic distance matrix. Aiming at the problem that the landmark points are introduced into iterative optimization when distance matrix is processed, a method of vector quantization is employed instead of the traditional random selection. Thus, the whole manifold structure is indicated better by the selected samples. Finally, the radial function is regarded as basis, and consequently explicit mapping of dimensionality reduction method is obtained. On the handwritten digits sets and UCI datasets, the experimental results show that the proposed algorithm is fast and stable with a higher recognition rate.
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2015, Vol. 28 
